Data-Agnostic? See How Data Quality Affects ROI in This A/B Test

If you consider various levels of quality—cars with lots of speed and no torque, or kitchen appliances with cheap motors—it’s easy to agree that quality affects user experience, good or bad, in a big way. Fast, friendly service at a restaurant can improve your dinner experience as much as a perfectly cooked steak.

Data is no different. Quality is a factor, and accuracy matters a lot.

The problem with data is that it changes slowly over time, decaying, typically at minimum, 30% per year. Its diminished quality isn’t as obvious as a food processor motor billowing acrid smoke (immediately after the warranty expires, of course).

The last thing you want is to spin cycles on bad data until your sales and marketing teams wear out.

In the same manner, many sales intelligence data providers claim to have high levels of accuracy. But unlike the perfect temperature of a steak, which is a matter of taste, data accuracy can be quantified objectively.

In Account-Based Sales Development (ABSD), data quality is critical for scaling effectively. It’s easy to see the correlation between data accuracy and sales efforts: If phone numbers are correct and link sales reps directly to their buyers, they can spend more time talking to likely prospects at the other end of the line, rather than shaking down a phone tree of gatekeepers. If Sales talks to more buyers, they will book more meetings, deliver more demos, and close deals faster.

That’s how ABSD is scaled, in part—and it is critically dependent upon data quality.

Many data providers claim to have the “most accurate” or “most actionable” business intelligence on the market. At DiscoverOrg, we provide current, human-verified intelligence, and we stand behind it like no other provider can: We contractually guarantee 95% accuracy of our data.

While an accuracy guarantee sounds nice, it only matters if accuracy is tied to higher performance.

Setting up the A/B data test

Parameters of the A/B test were planned by Intelemark’s own COO, Ed Berman, whose career includes deep study of statistical design and experimentation. The experiment involved 264 hours of phone time and initiated 5307 phone calls.

Variables monitored included inputs such as the Number of Dials To Reach Correct Contact, and outcomes such as Wrong Numbers, Reached Contacts, and many more.

Intelemark agents were given lead lists of identical criteria and length from both DiscoverOrg and the other data provider.

Lead lists were scrubbed of information identifying which database they originated from. Agents were provided with only the account name and the contact’s title and phone number.

Implications

Turns out, just like the quality of your former food processor, you can’t always rely on the labels and guarantees on packaging to accurately represent quality.

DiscoverOrg data is contractually guaranteed to be at least 95% accurate, and our live team of 250+ researchers refreshes our data every 60 days to achieve that. Our emails and phone numbers are verified with a rigorous testing and validation process.

The results of the test were impressive! Researchers using DiscoverOrg data were stopped by gatekeepers half as often as those using sales data from the other leading data provider. No matter how you slice it, after over 260 hours of phone calls, researchers had over four times more success using DiscoverOrg data—and reached out to 15% fewer accounts. When fewer calls lead to such a significantly higher rate of success, it’s hard make an argument against using more accurate data.